A look at the latest Jedlovec House eletricity usage and solar
production from PPL and Enphase.
Load packages
library(tidyverse)
library(lubridate)
library(hms)
library(readxl)
Load PPL data
Transform PPL Data
hourly_ppl_pivot <- ppl_15mins %>%
rename(date = Date) %>%
pivot_longer(!c("Account Number", "Meter Number", date, "Read Type", Min, Max, Total), names_to = "time", values_to = "kWh")
rename(ppl_15mins, date = Date)
hourly_ppl_pivot <- hourly_ppl_pivot %>%
mutate(time = parse_time(time, '%H:%M %p'), month = month(date, label=TRUE), year = year(date), yday = yday(date), wday = wday(date, label=TRUE))
(hourly_ppl_net <- hourly_ppl_pivot %>%
filter(`Read Type` == "kWh Net"))
Load Enphase data
import <- read_csv("enphase_history_230701.csv")
Rows: 42432 Columns: 2── Column specification ────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (1): Date/Time
dbl (1): Energy Produced (Wh)
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
update <- read_csv("hourly_generation_230625_240510.csv")
Rows: 30816 Columns: 2── Column specification ────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (1): Date/Time
dbl (1): Energy Produced (Wh)
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
update <- update %>%
mutate(`Date/Time` = as.POSIXct(`Date/Time`,format="%m/%d/%Y %H:%M",tz=Sys.timezone())) %>%
filter(`Date/Time` >= as.POSIXct('07/01/2023 00:00',format="%m/%d/%Y %H:%M",tz=Sys.timezone()) )
import <- import %>%
mutate(`Date/Time` = as.POSIXct(`Date/Time`,format="%m/%d/%Y %H:%M",tz=Sys.timezone()))
full_hist <- rbind(import,update)
full_hist %>% arrange(desc(`Date/Time`))
(hourly_production <- full_hist %>%
rename(datetime = `Date/Time`, energy_produced_Wh = `Energy Produced (Wh)`) %>%
mutate(datetime = as.POSIXct(datetime,format="%m/%d/%Y %H:%M",tz=Sys.timezone())) %>%
mutate(date = date(datetime), time = as.hms(format(datetime, format = "%H:%M:%S")), month = month(datetime, label=TRUE), year = year(datetime), day = day(datetime), yday = yday(datetime), monthday = format(datetime, "%m-%d"), wday = wday(datetime, label=TRUE), equinox_day = (yday + 10) %% 365, equinox_group = floor((equinox_day+15)/30)*30)
)
NA
Spot-check Enphase Should see lifetime production by day and by
hour
ggplot(hourly_production, aes(datetime, energy_produced_Wh)) +
geom_point()

ggplot(hourly_production, aes(time, energy_produced_Wh)) +
theme(axis.text.x = element_text(angle = 90)) +
geom_point()

Net + Produced = Consumed
Calculate production for first year of solar panels, second year,
etc.
hourly_electricity <- hourly_electricity %>%
mutate(solar_year = factor(case_when(
datetime < as.POSIXct('04/16/2022 00:00',format="%m/%d/%Y %H:%M",tz=Sys.timezone()) ~ 0,
datetime >= as.POSIXct('04/16/2022 00:00',format="%m/%d/%Y %H:%M",tz=Sys.timezone()) &
datetime < as.POSIXct('04/16/2023 00:00',format="%m/%d/%Y %H:%M",tz=Sys.timezone()) ~ 1,
datetime >= as.POSIXct('04/16/2023 00:00',format="%m/%d/%Y %H:%M",tz=Sys.timezone()) &
datetime < as.POSIXct('04/16/2024 00:00',format="%m/%d/%Y %H:%M",tz=Sys.timezone()) ~ 2,
TRUE ~ 3)
)) %>%
mutate(yday = yday(date))
hourly_electricity %>%
group_by(solar_year) %>%
summarize(net_kWh = sum(net_kWh), produced_kWh = sum(produced_kWh), consumed_kWh = sum(consumed_kWh))
NA
Interesting! Produced kWh went down by 5%, but consumed kWh went down
by 10% despite the fact that we got an electric car! Let’s explore that
further.
daily_electricity <- hourly_electricity %>%
group_by(solar_year, yday) %>%
summarize(net_kWh = sum(net_kWh), produced_kWh = sum(produced_kWh), consumed_kWh = sum(consumed_kWh))
`summarise()` has grouped output by 'solar_year'. You can override using the `.groups` argument.
ggplot(daily_electricity, aes(yday, consumed_kWh, group=solar_year, color=solar_year)) +
geom_point() +
geom_smooth(span=0.3) +
scale_x_continuous(breaks = c(1, 32, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335), labels = month.abb) +
theme(axis.text.x = element_text(angle = 90))


Look how our electricity consumption got much less predictable, more
uneven, after we purchased an EV and installed a level 2 charger in late
January/early February 2023!
We probably need to filter out the EV charging and compare that
separately.
hourly_electricity %>% summarize(min_date = min(date), max_date = max(date))
electricity_by_time <- hourly_electricity %>%
filter(solar_year == 1 | solar_year == 2) %>%
#filter(date <= as.POSIXct('12/31/2022 00:00',format="%m/%d/%Y %H:%M",tz=Sys.timezone()) ) %>%
group_by(time, solar_year) %>%
summarize(produced_kWh = mean(produced_kWh), consumed_kWh = mean(consumed_kWh), net_kWh = mean(net_kWh)) %>%
arrange(time)
`summarise()` has grouped output by 'time'. You can override using the `.groups` argument.
electricity_by_time
ggplot(electricity_by_time, aes(x=time, y=consumed_kWh, group=solar_year, color=solar_year)) +
geom_point()

#labs(colour="",x="Time of Day",y="Electricity Consumption (kWh)")+
#scale_color_manual(values = c("red","black","green")) +
#ggtitle("Home Electricity in 15-Minute Intervals (Since April 15, 2022)")
Wow, look how much less was used during the day in year 2!!! Like a
50% reduction!
Is this due to milder weather?
Let’s look at it by month:
#electricity_by_month <-
hourly_electricity %>%
mutate(month = month(date)) %>%
group_by(solar_year, month) %>%
summarize(consumed_kWh = mean(consumed_kWh)) %>%
arrange(month, solar_year) %>%
pivot_wider(names_from = month, values_from = consumed_kWh) %>%
arrange(solar_year)
`summarise()` has grouped output by 'solar_year'. You can override using the `.groups` argument.
#Look at limited window during day (no EV charging)
# hourly_electricity %>%
# filter(time == '12:00:00') %>%
# #filter(time > as.POSIXct('04:00:00',format="%H:%M:$s",tz=Sys.timezone()) ) %>% #& time < as.POSIXct('18:00',format="%H:%M",tz=Sys.timezone())) %>%
# mutate(month = month(date)) %>%
# group_by(solar_year, month) %>%
# summarize(consumed_kWh = mean(consumed_kWh)) %>%
# arrange(month, solar_year) %>%
# pivot_wider(names_from = month, values_from = consumed_kWh) %>%
# arrange(solar_year)
Look at specific months
electricity_by_month_time <- hourly_electricity %>%
mutate(month = as_factor(month(date))) %>%
filter(solar_year == 1 | solar_year == 2) %>%
filter(month == 10) %>%
group_by(solar_year, month, time) %>%
summarize(produced_kWh = mean(produced_kWh), consumed_kWh = mean(consumed_kWh), net_kWh = mean(net_kWh)) %>%
arrange(solar_year, month, time)
`summarise()` has grouped output by 'solar_year', 'month'. You can override using the `.groups` argument.
ggplot(electricity_by_month_time, aes(time, consumed_kWh, group=solar_year, color=solar_year)) +
#facet_grid(rows = vars(month)) +
geom_point(size=0.5) +
ggtitle("Home Electricity in 15-Minute Intervals") +
ylab("Net Electricity Consumption (kWh)")

Example day with EV charging
sample_day <- hourly_electricity %>%
filter(solar_year == 1 | solar_year == 2) %>%
filter(yday == 157) %>%
group_by(date, solar_year, yday, time) %>%
summarize(produced_kWh = mean(produced_kWh), consumed_kWh = mean(consumed_kWh), net_kWh = mean(net_kWh)) %>%
arrange(date, solar_year, yday, time)
`summarise()` has grouped output by 'date', 'solar_year', 'yday'. You can override using the `.groups` argument.
ggplot(sample_day, aes(time, consumed_kWh, group=solar_year, color=solar_year)) +
#facet_grid(rows = vars(month)) +
geom_point(size=0.5) +
ggtitle("Home Electricity in 15-Minute Intervals") +
ylab("Electricity Consumption (kWh)")

Ok, let’s detect and remove EV charging sessions.
bcp
$x
[1] -0.4717343 0.7711507 -1.5990938 -0.7508534 0.8046654 -0.6484233 0.1813083 0.7249717 -0.6922743
[10] 1.0614834 3.6704000 4.0869019 4.0584831 4.9843196 5.1803859 5.1696722 5.0958858 5.5062540
[19] 5.3078164 5.3926576
$max_p
[,1] [,2] [,3]
[1,] 0.9009547 0 0
[2,] 0.8015196 0 1
[3,] 0.7616943 0 2
[4,] 0.7018043 0 3
[5,] 0.6985363 0 4
[6,] 0.6588125 0 5
[7,] 0.5838624 0 6
[8,] 0.6049459 0 7
[9,] 0.5252828 0 8
[10,] 0.3619282 0 9
[11,] 0.4815652 0 10
[12,] 0.5369016 10 0
[13,] 0.9141713 10 1
[14,] 0.9182526 10 2
[15,] 0.9211320 10 3
[16,] 0.9242694 10 4
[17,] 0.9249073 10 5
[18,] 0.9268006 10 6
[19,] 0.9281916 10 7
[20,] 0.0000000 0 0
$parameters
theta alpha beta th_cp
0.9 1.0 1.0 0.5
$series_length
[1] 20
$result
NULL
attr(,"class")
[1] "BayesCP"
---
title: "Electricity/Solar Update"
output: html_notebook
---

A look at the latest Jedlovec House eletricity usage and solar production from PPL and Enphase.

Load packages
```{r}
library(tidyverse)
library(lubridate)
library(hms)
library(readxl)
library(onlineBcp)
```

Load PPL data
```{r}

col_datatypes <- c('numeric','numeric','date','text',rep('numeric',99))

hourly1 <- read_excel("Hourly Usage 220326 to 220624.xlsx", col_types = col_datatypes)
hourly2 <- read_excel("Hourly Usage 220625 to 230623.xlsx", col_types = col_datatypes)
hourly3 <- read_excel("PPL 230624 to 240509.xlsx", col_types = col_datatypes)

#hourly1
#hourly2
#hourly3

ppl_15mins <- bind_rows(hourly1,list(hourly2,hourly3))

ppl_15mins %>% arrange(desc(Date), `Read Type`)


```

Transform PPL Data
```{r}

hourly_ppl_pivot <- ppl_15mins %>% 
  rename(date = Date) %>% 
  pivot_longer(!c("Account Number", "Meter Number", date, "Read Type", Min, Max, Total), names_to = "time", values_to = "kWh") 

rename(ppl_15mins, date = Date)

hourly_ppl_pivot <- hourly_ppl_pivot %>% 
  mutate(time = parse_time(time, '%H:%M %p'), month = month(date, label=TRUE), year = year(date), yday = yday(date), wday = wday(date, label=TRUE))

(hourly_ppl_net <- hourly_ppl_pivot %>% 
  filter(`Read Type` == "kWh Net"))
```

Load Enphase data
```{r}
import <- read_csv("enphase_history_230701.csv")
update <- read_csv("hourly_generation_230625_240510.csv")

update <- update %>% 
  mutate(`Date/Time` = as.POSIXct(`Date/Time`,format="%m/%d/%Y %H:%M",tz=Sys.timezone())) %>% 
  filter(`Date/Time` >= as.POSIXct('07/01/2023 00:00',format="%m/%d/%Y %H:%M",tz=Sys.timezone()) )

import <- import %>% 
  mutate(`Date/Time` = as.POSIXct(`Date/Time`,format="%m/%d/%Y %H:%M",tz=Sys.timezone())) 

full_hist <- rbind(import,update)

full_hist %>% arrange(desc(`Date/Time`))

(hourly_production <- full_hist %>% 
  rename(datetime = `Date/Time`, energy_produced_Wh = `Energy Produced (Wh)`) %>% 
  mutate(datetime = as.POSIXct(datetime,format="%m/%d/%Y %H:%M",tz=Sys.timezone())) %>% 
  mutate(date = date(datetime), time = as.hms(format(datetime, format = "%H:%M:%S")), month = month(datetime, label=TRUE), year = year(datetime), day = day(datetime), yday = yday(datetime), monthday = format(datetime, "%m-%d"), wday = wday(datetime, label=TRUE), equinox_day = (yday + 10) %% 365, equinox_group = floor((equinox_day+15)/30)*30)
)

```

Spot-check Enphase
Should see lifetime production by day and by hour
```{r}
ggplot(hourly_production, aes(datetime, energy_produced_Wh)) +
  geom_point()

ggplot(hourly_production, aes(time, energy_produced_Wh)) +
  theme(axis.text.x = element_text(angle = 90)) +
  geom_point()

```

Net + Produced = Consumed

```{r}
# hourly_ppl_net <- hourly_ppl_net %>% mutate(date = as_date(date))

#hourly_ppl_net %>% arrange(desc(date))
#hourly_production %>% arrange(desc(date))

(hourly_electricity <- hourly_ppl_net %>% 
    inner_join(hourly_production, by = join_by(date,time))  %>% 
    mutate(consumed_kWh = kWh + energy_produced_Wh/1000, produced_kWh = energy_produced_Wh/1000)  %>% 
    rename(net_kWh = kWh) %>% 
    select(datetime, date, time, net_kWh, produced_kWh, consumed_kWh) %>%
    arrange(date))

```

Calculate production for first year of solar panels, second year, etc.

```{r}

hourly_electricity <- hourly_electricity %>% 
  mutate(solar_year = factor(case_when(
                        datetime < as.POSIXct('04/16/2022 00:00',format="%m/%d/%Y %H:%M",tz=Sys.timezone()) ~ 0,
                        datetime >= as.POSIXct('04/16/2022 00:00',format="%m/%d/%Y %H:%M",tz=Sys.timezone())  &
                          datetime < as.POSIXct('04/16/2023 00:00',format="%m/%d/%Y %H:%M",tz=Sys.timezone()) ~ 1,
                        datetime >= as.POSIXct('04/16/2023 00:00',format="%m/%d/%Y %H:%M",tz=Sys.timezone())  &
                          datetime < as.POSIXct('04/16/2024 00:00',format="%m/%d/%Y %H:%M",tz=Sys.timezone()) ~ 2,
                        TRUE ~ 3)
         )) %>% 
  mutate(yday = yday(date))

hourly_electricity %>% 
  group_by(solar_year) %>% 
  summarize(net_kWh = sum(net_kWh), produced_kWh = sum(produced_kWh), consumed_kWh = sum(consumed_kWh))

```

Interesting! Produced kWh went down by 5%, but consumed kWh went down by 10% despite the fact that we got an electric car! Let's explore that further. 

```{r}
daily_electricity <- hourly_electricity %>% 
  group_by(solar_year, yday) %>% 
  summarize(net_kWh = sum(net_kWh), produced_kWh = sum(produced_kWh), consumed_kWh = sum(consumed_kWh))

ggplot(daily_electricity, aes(yday, consumed_kWh, group=solar_year, color=solar_year)) + 
  geom_point() +
  geom_smooth(span=0.3) +
  scale_x_continuous(breaks = c(1, 32, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335), labels = month.abb) +
  theme(axis.text.x = element_text(angle = 90)) 

```


```{r}
daily_electricity <- hourly_electricity %>% 
  mutate(solar_day = interval(as_date(as.POSIXct('04/15/2022',format="%m/%d/%Y",tz=Sys.timezone())),as_date(date)) / days(1) 
         ) %>% 
  group_by(date, solar_day) %>% 
  summarize(net_kWh = sum(net_kWh), produced_kWh = sum(produced_kWh), consumed_kWh = sum(consumed_kWh))

ggplot(daily_electricity, aes(date, consumed_kWh)) + 
  geom_point() +
  #scale_x_continuous(breaks = c(1, 32, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335), labels = month.abb) +
  theme(axis.text.x = element_text(angle = 90)) 


```
Look how our electricity consumption got much less predictable, more uneven, after we purchased an EV and installed a level 2 charger in late January/early February 2023!

We probably need to filter out the EV charging and compare that separately. 


```{r}

#hourly_electricity %>% summarize(min_date = min(date), max_date = max(date))

electricity_by_time <- hourly_electricity %>% 
  filter(solar_year == 1 | solar_year == 2) %>% 
  #filter(date <= as.POSIXct('12/31/2022 00:00',format="%m/%d/%Y %H:%M",tz=Sys.timezone()) ) %>% 
  group_by(time, solar_year) %>% 
  summarize(produced_kWh = mean(produced_kWh), consumed_kWh = mean(consumed_kWh), net_kWh = mean(net_kWh)) %>% 
  arrange(time)

electricity_by_time

ggplot(electricity_by_time, aes(x=time, y=consumed_kWh, group=solar_year, color=solar_year)) +
  geom_point() 
  #labs(colour="",x="Time of Day",y="Electricity Consumption (kWh)")+
  #scale_color_manual(values = c("red","black","green")) +
  #ggtitle("Home Electricity in 15-Minute Intervals (Since April 15, 2022)")

```
Wow, look how much less was used during the day in year 2!!! Like a 50% reduction! 

Is this due to milder weather? 

Let's look at it by month: 

```{r}
#electricity_by_month <- 
hourly_electricity %>% 
  mutate(month = month(date)) %>% 
  group_by(solar_year, month) %>% 
  summarize(consumed_kWh = mean(consumed_kWh)) %>% 
  arrange(month, solar_year) %>% 
  pivot_wider(names_from = month, values_from = consumed_kWh) %>% 
  arrange(solar_year)

#Look at limited window during day (no EV charging)
 
# hourly_electricity %>% 
#   filter(time == '12:00:00') %>% 
#   #filter(time > as.POSIXct('04:00:00',format="%H:%M:$s",tz=Sys.timezone()) ) %>%  #& time < as.POSIXct('18:00',format="%H:%M",tz=Sys.timezone())) %>% 
#   mutate(month = month(date)) %>% 
#   group_by(solar_year, month) %>% 
#   summarize(consumed_kWh = mean(consumed_kWh)) %>% 
#   arrange(month, solar_year) %>% 
#   pivot_wider(names_from = month, values_from = consumed_kWh) %>% 
#   arrange(solar_year)


```

Look at specific months

```{r}
electricity_by_month_time <- hourly_electricity %>% 
  mutate(month = as_factor(month(date))) %>% 
  filter(solar_year == 1 | solar_year == 2) %>% 
  filter(month == 10) %>% 
  group_by(solar_year, month, time) %>% 
  summarize(produced_kWh = mean(produced_kWh), consumed_kWh = mean(consumed_kWh), net_kWh = mean(net_kWh)) %>% 
  arrange(solar_year, month, time)

ggplot(electricity_by_month_time, aes(time, consumed_kWh, group=solar_year, color=solar_year)) +
  #facet_grid(rows = vars(month)) +
  geom_point(size=0.5) +
  ggtitle("Home Electricity in 15-Minute Intervals") +
  ylab("Electricity Consumption (kWh)")

```

Example day with EV charging

```{r}
sample_day <- hourly_electricity %>% 
  filter(solar_year == 1 | solar_year == 2) %>% 
  filter(yday == 157) %>% 
  group_by(date, solar_year, yday, time) %>% 
  summarize(produced_kWh = mean(produced_kWh), consumed_kWh = mean(consumed_kWh), net_kWh = mean(net_kWh)) %>% 
  arrange(date, solar_year, yday, time)

ggplot(sample_day, aes(time, consumed_kWh, group=solar_year, color=solar_year)) +
  #facet_grid(rows = vars(month)) +
  geom_point(size=0.5) +
  ggtitle("Home Electricity in 15-Minute Intervals") +
  ylab("Electricity Consumption (kWh)")

```

Ok, let's detect and remove EV charging sessions. 

```{r}
# library(onlineBcp)
x <- c(rnorm(10, 0, 1), rnorm(10, 5, 1))
bcp <- online_cp(x)
summary(bcp)

bcp

```

```{r}
# library(onlineBcp)
x <- sample_day %>% 
  filter(solar_year == 2) %>% 
  ungroup() %>% 
  select(consumed_kWh)

bcp <- online_cp(x)

summary(bcp)

bcp

ggplot(sample_day, aes(time, consumed_kWh, group=solar_year, color=solar_year)) +
  #facet_grid(rows = vars(month)) +
  geom_point(size=0.5) +
  ggtitle("Home Electricity in 15-Minute Intervals") +
  ylab("Electricity Consumption (kWh)")
```


